#!/bin/bash

################基础配置参数，需要模型审视修改##################
# 必选字段(必须在此处定义的参数): Network batch_size 
# 网络名称，同目录名称
Network="ResNet50_ID4149_for_PyTorch"

workers=256
# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    elif [[ $para == --batch_size* ]];then
        batch_size=`echo ${para#*=}`
    elif [[ $para == --lr* ]];then
        lr=`echo ${para#*=}`
    elif [[ $para == --train_epochs* ]];then
        train_epochs=`echo ${para#*=}`
    elif [[ $para == --world_size* ]];then
        world_size=`echo ${para#*=}`
    elif [[ $para == --node_rank* ]];then
        node_rank=`echo ${para#*=}`
    elif [[ $para == --master_addr* ]];then
        master_addr=`echo ${para#*=}`
    elif [[ $para == --hf32 ]];then
      	hf32=`echo ${para#*=}`
      	export ALLOW_HF32=True
    elif [[ $para == --fp32 ]];then
      	fp32=`echo ${para#*=}`
      	export ALLOW_FP32=True
    fi
done

#HCCL白名单开关,1-关闭/0-开启。设置为1则无需校验HCCL通信白名单。
export HCCL_WHITELIST_DISABLE=1
export HCCL_IF_IP=$(hostname -I |awk '{print $1}')

# 校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi


###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_diename=${cur_path##*/}
if [ x"${cur_path_last_diename}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################创建日志输出目录，不需要修改#################
ASCEND_DEVICE_ID=0
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi


#################启动训练脚本#################
#训练开始时间，不需要修改
start_time=$(date +%s)
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi

RANK_ID_START=0

KERNEL_NUM=$(($(nproc)/8))
for((RANK_ID=0;RANK_ID<8;RANK_ID++));
do
    export RANK_SIZE=8
    export RANK_ID=$RANK_ID
    if [ $(uname -m) = 'aarch64' ]
    then
        PID_START=$((KERNEL_NUM * RANK_ID))
        PID_END=$((PID_START + KERNEL_NUM - 1))
        nohup taskset -c $PID_START-$PID_END python3 main.py \
            --data ${data_path} \
            --addr ${master_addr} \
            --amp \
            --world-size ${world_size} \
            --seed 60 \
            -a resnet50 \
            -j ${workers} \
            -b ${batch_size}  \
            --lr ${lr} \
            --epochs ${train_epochs} \
            --gpu ${RANK_ID} \
            --rank ${node_rank} \
            --multiprocessing-distributed > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
    else
        nohup python3 main.py \
            --data ${data_path} \
            --addr ${master_addr} \
            --amp \
            --world-size ${world_size} \
            --seed 60 \
            -a resnet50 \
            -j ${workers} \
            -b ${batch_size}  \
            --lr ${lr} \
            --epochs ${train_epochs} \
            --gpu ${RANK_ID} \
            --rank ${node_rank} \
            --multiprocessing-distributed > ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log 2>&1 &
    fi
done

wait

if [[ x"${master_addr}" == x"${HCCL_IF_IP}" ]];then
    ##################获取训练数据################
    # 训练结束时间，不需要修改
    end_time=$(date +%s)
    e2e_time=$(( $end_time - $start_time ))

    # 结果打印，不需要修改
    echo "------------------ Final result ------------------"
    # 输出性能FPS，需要模型审视修改
    step_time=`grep "Epoch" ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log | awk -F "Time " '{print $2}' | awk -F " " '{print $1}' | tail -n 10 | awk '{a+=$1} END {if (NR != 0) printf("%.3f",a/NR)}'`
    FPS=`echo "${batch_size} / ${step_time}"|bc`
    # 打印，不需要修改
    echo "Final Performance images/sec : $FPS"

    # 输出训练精度,需要模型审视修改
    train_accuracy=`grep -a '*   Acc@1'  ${test_path_dir}/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log|awk 'END {print}'|awk -F "Acc@1 " '{print $NF}'|awk -F " " '{print $1}'`
    # 打印，不需要修改
    echo "Final Train Accuracy : ${train_accuracy}"
    echo "E2E Training Duration sec : $e2e_time"

    # 训练用例信息，不需要修改
    BatchSize=${batch_size}
    DeviceType=`uname -m`
    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
    # 吞吐量
    ActualFPS=${FPS}
    # 单迭代训练时长
    TrainingTime=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'*1000/'${FPS}'}'`

    # 从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
    grep Epoch: ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|grep -v Test|awk -F "Loss " '{print $NF}' | awk -F " " '{print $1}' >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

    # 最后一个迭代loss值，不需要修改
    ActualLoss=`awk 'END {print}'  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

    # 关键信息打印到${CaseName}.log中，不需要修改
    echo "Network = ${Network}" >  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "RankSize = ${RANK_SIZE}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "BatchSize = ${BatchSize}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "DeviceType = ${DeviceType}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "CaseName = ${CaseName}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "ActualFPS = ${ActualFPS}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "TrainingTime = ${TrainingTime}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "TrainAccuracy = ${train_accuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "ActualLoss = ${ActualLoss}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
    echo "E2ETrainingTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
fi